IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i1p362-d1560925.html
   My bibliography  Save this article

Research on the Thermal Comfort Experience of Metro Passengers Under Sustainable Transportation: Theory of Stimulus-Organism-Response Integration with a Technology Acceptance Model

Author

Listed:
  • Tao Zou

    (School of Architecture and Art, Central South University, Changsha 410083, China
    Lushan Laboratory, Changsha 410128, China)

  • Jiawei Guan

    (School of Architecture and Art, Central South University, Changsha 410083, China)

  • Yuhui Wang

    (School of Architecture and Art, Central South University, Changsha 410083, China
    Lushan Laboratory, Changsha 410128, China)

  • Fangyuan Zheng

    (School of Architecture and Art, Central South University, Changsha 410083, China)

  • Yuwen Lin

    (School of Architecture and Art, Central South University, Changsha 410083, China)

  • Yifan Zhao

    (School of Architecture and Art, Central South University, Changsha 410083, China)

Abstract

(1) Background: Metro is an important part of urban transportation, carrying huge passenger volume every day. With improvements in people’s living standards, passengers’ demand for a comfortable Metro experience is increasing. In the context of urban development, maintaining a good thermal comfort level of Metro cars is not only conducive to providing a comfortable and healthy environment for passengers, but also has great significance for reducing energy consumption and sustainable urban transportation development. This study provides empirical evidence for Metro design and operation strategies, aiming at creating a safer and more comfortable passenger experience. (2) Methods: By combining passengers’ comfort perception (cognitive value of thermal environment) and rideability perception (confidence in thermal comfort control), this study established a correlation model between thermal comfort and passenger unsafe behavior, namely the integration of SOR (Stimulus-Organism-Response) and TAM (Technology Acceptance Model). This study used methods such as field surveys, structural equation modeling, and reliability and validity analyses to investigate the impact of Metro thermal comfort on passenger behavior safety. (3) Results: This study found that the Metro thermal environment, including temperature, humidity, and airflow velocity, significantly affects passengers’ comfort perception and behavior choices. (4) Conclusions: Passengers may exhibit avoidance behavior in uncomfortable thermal environments, leading to uneven distribution of people in the train car and increasing safety risks. Improving Metro thermal environments can effectively enhance passengers’ perceived comfort and reduce unsafe behavior motivation, which is of great significance for safe Metro operations.

Suggested Citation

  • Tao Zou & Jiawei Guan & Yuhui Wang & Fangyuan Zheng & Yuwen Lin & Yifan Zhao, 2025. "Research on the Thermal Comfort Experience of Metro Passengers Under Sustainable Transportation: Theory of Stimulus-Organism-Response Integration with a Technology Acceptance Model," Sustainability, MDPI, vol. 17(1), pages 1-30, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:362-:d:1560925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/1/362/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/1/362/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Seriani & Vicente Aprigliano & Shirley Gonzalez & Gabriela Baeza & Ariel Lopez & Taku Fujiyama, 2024. "The Effect of Seat Layout on the Interaction of Passengers Inside the Train Carriage: An Experimental Approach for Urban Services," Sustainability, MDPI, vol. 16(3), pages 1-15, January.
    2. Kaori Tamura & Sayaka Matsumoto & Yu Hsuan Tseng & Takayuki Kobayashi & Jun’ichi Miwa & Ken’ichi Miyazawa & Toyotaka Hirao & Soichiro Matsumoto & Seiji Hiramatsu & Hiroyuki Otake & Tsuyoshi Okamoto, 2021. "Physiological and subjective comfort evaluation under different airflow directions in a cooling environment," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-28, April.
    3. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianmin Wang & Xiaoying Wen & Shikang Zhou & Zhihong Zhang & Dongye Zhao, 2025. "Towards Sustainable Human–Land Symbiosis: An Empirical Study of Chinese Traditional Villages," Land, MDPI, vol. 14(8), pages 1-21, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeideh Sharifi fard & Ezhar Tamam & Md Salleh Hj Hassan & Moniza Waheed & Zeinab Zaremohzzabieh, 2016. "Factors affecting Malaysian university students’ purchase intention in social networking sites," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1182612-118, December.
    2. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    3. Philippe Cohard, 2020. "Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions," Post-Print hal-02987225, HAL.
    4. Melih Engin & Fatih Gürses, 2019. "Adoption of Hospital Information Systems in Public Hospitals in Turkey: An Analysis with the Unified Theory of Acceptance and Use of Technology Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-19, October.
    5. Morosan, Cristian, 2016. "An empirical examination of U.S. travelers’ intentions to use biometric e-gates in airports," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 120-128.
    6. Abdesamad Zouine & Pierre Fenies, 2014. "The Critical Success Factors Of The ERP System Project: A Meta-Analysis Methodology," Post-Print hal-01419785, HAL.
    7. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    8. Sarv Devaraj & Robert F. Easley & J. Michael Crant, 2008. "Research Note ---How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use," Information Systems Research, INFORMS, vol. 19(1), pages 93-105, March.
    9. Claudio Vitari & Elisabetta Raguseo, 2016. "Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data," Post-Print halshs-01923271, HAL.
    10. Chen-Yuan Chen & Bih-Yaw Shih & Shih-Hsien Yu, 2012. "Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(3), pages 1217-1231, July.
    11. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    12. Fatima Zahra Barrane & Gahima Egide Karuranga & Diane Poulin, 2018. "Technology Adoption and Diffusion: A New Application of the UTAUT Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-19, December.
    13. Mary D. Adu & Usman H. Malabu & Aduli E. O. Malau-Aduli & Aaron Drovandi & Bunmi S. Malau-Aduli, 2020. "Efficacy and Acceptability of My Care Hub Mobile App to Support Self-Management in Australians with Type 1 or Type 2 Diabetes," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
    14. Ofir Turel & Catherine E. Connelly, 2012. "Team Spirit: The Influence of Psychological Collectivism on the Usage of E-Collaboration Tools," Group Decision and Negotiation, Springer, vol. 21(5), pages 703-725, September.
    15. Baicu Claudia Gabriela & Gârdan Iuliana Petronela & Gârdan Daniel Adrian & Epuran Gheorghe, 2020. "The impact of COVID-19 on consumer behavior in retail banking. Evidence from Romania," Management & Marketing, Sciendo, vol. 15(s1), pages 534-556, October.
    16. Cong Cheng & Hongfang Cui, 2024. "Combining digital and legacy technologies: firm digital transformation strategies—evidence from Chinese manufacturing companies," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    17. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    18. Zamrudi Zakky & Wicaksono Teguh, 2018. "Promoting the Use of Social Commerce on SME in the Context of Logistics: UTAUT Model Examination," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 9(2), pages 73-82, November.
    19. Yuting Zhang & Xiaofen Yu & Ning Cai & Yong Li, 2020. "Analyzing the Employees’ New Media Use in the Energy Industry：The Role of Creative Self-Efficacy, Perceived Usefulness and Leaders’ Use," Sustainability, MDPI, vol. 12(3), pages 1-13, January.
    20. Cliff R. Kikawa & Charity Kiconco & Moses Agaba & Dimas Ntirampeba & Amos Ssematimba & Billy M. Kalema, 2022. "Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model," Sustainability, MDPI, vol. 14(21), pages 1-20, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:362-:d:1560925. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.